While the term “healthcare consumerism” has been used since the 1930s, today the term refers to the importance of creating a more patient or consumer-centered experience. Patients want a more integrated, seamless healthcare experience that focuses on their particular needs. Artificial intelligence (A.I.), big tech, and big data give patients more transparency, more choice, and more flexibility across the healthcare ecosystem, which helps to facilitate a more positive healthcare experience.
But the use of A.I. and machine learning to improve the patient experience, particularly and most importantly in treatment outcomes, begins long before the application of telemedicine, online appointment setting, digitalization, access to real-time information and price transparency, all of which are being used within the ecosystem with varying degrees of success.
Where does healthcare consumerism really begin? Not in the doctor’s office and not in the hospital. It begins in the laboratory. The Human Genome Project is foundational to the way we now diagnose and treat diseases. In a recent article titled “CRISPR-based diagnostics,” (Nature Biomedical Engineering, July 16, 2021), the author makes the point that the “accurate and timely diagnosis of disease is a preresquite for efficient therapeutic intervention.” Effective therapeutic intervention is arguably the most important part of the patient experience.
CRISPR (clustered regularly interspaced short palindromic repeats) uses trace amounts of DNA and RNA to more accurately diagnose diseases. CRISPR-based diagnostics “enable accurate testing at home, at the point of care and in the field.” This A.I.-driven technology is now the most commonly used to identify DNA and RNA biomarkers. Biomarkers, which are found in blood, body fluids, and tissue, provide a guidepost to determine how a patient will respond to treatment for a particular disease or condition. Moreover, CRISPR technology can be used to edit genes and thus as one scientist put it, “will likely change the world.”
Lantern Pharma, a Nasdaq-listed company, uses an A.I.-driven platform called RADR™, which analyzes over 8 billion data points to develop drugs to treat cancer. By analyzing vast amounts of pre-clinical and clinical data, their platform can determine which biomarkers will respond best to a particular drug. Biomarker-based clinical trials are 12 times more likely to succeed. Companies using A.I. and machine learning to stratify patient populations into responders and non-responders to particular drugs are developing precision therapeutics which “de-risk clinical trials, while increasing the potential for FDA approval, greatly reducing the time to commercialization and significantly lowering the cost of developing drugs.”
If we can get better, more effective drugs to market faster and cheaper, patients will get better treatment outcomes and, ultimately, lower costs. Using DNA-based diagnostics and therapeutics to better treat diseases is called precision medicine. Ultimately, the ability to predict, diagnose and treat diseases accurately and quickly is the final arbiter in the patient healthcare experience.
Putting the right healthcare plan in place for patients is also critical to creating a better patient healthcare experience. Just as precision medicine is individualized to the patient, health plans also need to be individualized to the consumer. Insurance companies and self-insured companies often use health plan companies, or third-party administrators, to design and manage these plans. The smartest and most innovative companies in the space are using A.I. and machine learning to create patient-centered plans that engage the patient in the process, tailoring the plans to his or her individual needs. One such company that recently went public on the Nasdaq, Marpai Health, has developed an A.I. and machine learning platform to create a world-class healthcare management company.
Marpai’s platform uses proprietary algorithms to help patients understand actions they can take today to help prevent problems tomorrow while predicting near-term health issues such as Type 2 diabetes or knee surgery so that healthcare providers can intervene with early treatment, which ultimately helps reduce the cost of care. Additionally, Marpai uses A.I. to help consumers choose providers that best meet their individual needs based on quality, safety, and patient satisfaction ratings. After evaluating a consumer’s healthcare profile they provide medically trained care experts, a personal health assistant, to help simplify complex decisions and map the best journey across the care continuum.
With a simple app, consumers can book appointments, track deductibles, access information, receive reminders, and more. By analyzing massive amounts of data with deep learning, Marpai enables proactive, not reactive healthcare choices. Just as Lantern Pharma’s RADR™ is powered by a deep neural network brain that mimics the logic and learning of the human brain to identify the right drug for a particular patient, Marpai’s “SMART” engine anticipates and prevents health events by identifying health risks with speed and precision, creating a better, more patient-centric experience.
